DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claim 22 is rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor, or for pre-AIA the applicant regards as the invention.
Regarding claim 22, the occurrence of “the set of predicted channel metrics” in line 2 has no antecedent basis.
Claim Rejections – 35 USC§ 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1, 12, 15-17,19, 20, 22, 23 and 30 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Kela et al(US 2025/0096875 A1).
Regarding claim 1, Kela ‘875 teaches, an apparatus configured for wireless communication([0029], [0035] and Fig. 1, a UE communicating with network(eNB) wirelessly) , comprising: a memory comprising instructions; and one or more processors configured to execute the instructions and cause the apparatus to([0021] and Fig. 1, an apparatus comprising at least one processor and at least one memory for storing code):
obtain, from a network entity, a set of machine learning module configurations associated with a set of quality of service (QoS) types([0029], [0035], [0048] and Figs. 1-2, the UE receiving from the network node, pre-learned machine learning model data, such as Q-tables or neural network weights, to the UE to set up its machine learning model. Kela ‘875 further teaches providing the pre-learned machine learning data for each different QoS class or class of traffic flow); identify a QoS type, from the set of QoS types, for data scheduled to be communicated with the network entity([0031], [0048], the UE uses QoS flow identifiers(QFIs) to identify different QoS classes and their requirements for communication. The system identifies and determines the highest priority QoS data frows that the UE is actively transmitting or receiving);
select a machine learning module configuration from the set of machine learning module configurations based on the QoS type([0029], [0048] Figs. 2, 5, separate QoS classes or QoS class groups may use different models and the model used for determining the UE’s beam may be selected based on the UE’s highest-priority QoS data flow.[0029] also states that different models may be provided for different UE classes or traffic flows); perform one or more beam prediction procedures based on an output of a machine learning model indicated by the selected machine learning module configuration([0035], [0038], [0040] and Figs. 1-2, the UE using the pre-learned data provided by the network to initialize and execute its machine learning model. The UE then uses the output provided by the machine learning model to predict/select/determine the preferred beam to use); and
output, for transmission to the network entity, a report associated with the one or more beam prediction procedures([0035], [0038], [0048] and Fig. 1, the UE outputs and reports the machine-learning selected identifiers back to the base station(gNB). The beam reporting based on the machine learning model’s prediction is transmitted to the network node within a channel state information (CSI) report).
Regarding claim 12, Kela ‘875 teaches all of the claim limitations above, Kela ‘875 further teaches, wherein the report is output at a defined periodicity based on a report configuration associated with the QoS type([0041], [0042], [0048], outputting the CSI report at a defined periodicity(e.g. establishing a constantly 5 ms periodicity). It further teaches utilizing different CSI reporting parameters-including different reporting periodicity based on the models phase , where eh models and parameters are configured for each QoS class or QoS class group).
Regarding claim 15, Kela ‘875 teaches all of the claim limitations above, Kela ‘875 further teaches, wherein the QoS type indicates a data priority or a traffic type of the data scheduled to be communicated with the network entity([0031], [0048], defining the QoS types by data priority (e.g. highest priority QoS data flows) and traffic flow or traffic type, e.g. differentiating between eMBB full buffer traffic, URLLC and best effort QoS classes).
Regarding claim 16, Kela ‘875 teaches, a user equipment (UE) ([0029], [0035] and Fig. 1, a UE communicating with network(eNB) wirelessly), comprising: a transceiver; a memory comprising instructions; and one or more processors configured to execute the instructions and cause the UE to([0021] and Fig. 1, an apparatus comprising at least one processor and at least one memory for storing code): receive, via the transceiver and from a network entity, a set of machine learning module configurations associated with a set of quality of service (QoS) types([0029], [0035], [0048] and Figs. 1-2, the UE receiving from the network node, pre-learned machine learning model data, such as Q-tables or neural network weights, to the UE to set up its machine learning model. Kela ‘875 further teaches providing the pre-learned machine learning data for each different QoS class or class of traffic flow);
identify a QoS type, from the set of QoS types, for data scheduled to be communicated with a network entity([0031], [0048], the UE uses QoS flow identifiers(QFIs) to identify different QoS classes and their requirements for communication. The system identifies and determines the highest priority QoS data frows that the UE is actively transmitting or receiving); select a machine learning module configuration from the set of machine learning module configurations based on the QoS type([0029], [0048] Figs. 2, 5, separate QoS classes or QoS class groups may use different models and the model used for determining the UE’s beam may be selected based on the UE’s highest-priority QoS data flow.[0029] also states that different models may be provided for different UE classes or traffic flows);
perform one or more beam prediction procedures based on an output of a machine learning model indicated by the selected machine learning module configuration([0035], [0038], [0040] and Figs. 1-2, the UE using the pre-learned data provided by the network to initialize and execute its machine learning model. The UE then uses the output provided by the machine learning model to predict/select/determine the preferred beam to use); and transmit , via the transceiver and to the network entity, a report associated with the one or more beam prediction procedures([0035], [0038], [0048] and Fig. 1, the UE outputs and reports the machine-learning selected identifiers back to the base station(gNB). The beam reporting based on the machine learning model’s prediction is transmitted to the network node within a channel state information (CSI) report).
Regarding claim 17, Kela ‘875 teaches, an apparatus configured for wireless communication([0009], [0012] an Fig. 1, network node/gNB communicating wirelessly with UE), comprising: a memory comprising instructions; and one or more processors configured to execute the instructions and cause the apparatus to([0021] an Fig. 1, an apparatus(gNB) comprising at least one processor and memory for storing code): output, for transmission to a user equipment (UE), a set of machine learning module configurations associated with a set of quality of service (QoS) types([0011], [0029], [0035], [0048] and Figs. 1-2, the network node transmitting pre-learned ML model data, including a Q-table or neural-network weights, to the UE. The pre-learned machine learning data is provided for each different QoS class or traffic flow); and
obtain, from the UE, a report being based on a machine learning module configuration from the set of machine learning module configurations([0035] ,[0038], [0048] and Fig. 1, the network node receives a CSI report from the UE that includes the machine-learning-selected beam identifier(s).This reported beam selection is directly generated by and based on the machine learning model configured for the specific QoS class).
Regarding claim 19, Kela ‘875 teaches all of the claim limitations above, Kela ‘875 further teaches, wherein the one or more processors are further configured to cause the apparatus to: select a communication configuration associated with a QoS type from the set of QoS types([0047], [0048] and Fig. 5, selecting a transmission/reception (Tx/Rx) beam to use as the communication configuration for an upcoming transmission time interval(TTI) this configuration selection is based on the highest scheduling priority metric, which corresponds directly to the highest priority QoS class of the scheduled data); obtain, from the UE, data based on the communication configuration, or output, for transmission to the UE, other data based on the communication configuration ([0037], [0047] and Fig. 1, using the selected ML beam communication configuration for upcoming uplink (UL) or downlink(DL) data transmissions between the UE and the network node. For example, data is communicated over the physical channel (PUSCHO using the specific beam configuration established for the QoS type).
Regarding claim 20, Kela ‘875 teaches all of the claim limitations above, Kela ‘875 further teaches, wherein the communication configuration comprises a subset of beams of a first set of beams indicated by the report, wherein the data is obtained via the subset of beams or the other data is output via the subset of beams([0037], [0047] and figs. 1, 5, using the selected ML beam to perform both uplink (UL) data transmissions and downlink(DL) data receptions in the upcoming transmission time interval(TTI)).
Regarding claim 22, Kela ‘875 teaches all of the claim limitations above, Kela ‘875 further teaches, wherein the subset of beams includes a beam with a highest predicted channel metric in the set of predicted channel metrics based on the QoS type indicating normal priority([0040] and Fig. 2, ML model using Q-learning that calculates predicted reward(Q-values) for candidate beams and uses an “argmax” mathematical function to select the next beam action. The argmax function ensures that the selected beam for communication is the one associated with the highest predicted performance).
Regarding claim 23, Kela ‘875 teaches all of the claim limitations above, Kela ‘875 further teaches,, wherein the one or more processors are further configured to cause the apparatus to: decode the data based on the communication configuration([0079], [0080] and Fig. 6, a network node (gNB) receives data and decode received frames or messages).
Regarding claim 30, Kela ‘875 teaches all of the claim limitations above, Kela ‘875 further teaches, further comprising a transceiver configured to: transmit the set of machine learning module configurations([0035], [0048] and Fig. 1, a network nod includes a transceiver configured to transmit pre-learned machine learning model data, such as Q-tables or neural network weights, to UE); and receive the report([0035], [0048] and Fig. 1, the network node receives CSI reports from the UE via its transceiver. The reports include the preferred beam selections generated by the UE based on its machine learning configurations), wherein the apparatus is configured as a network entity ([0009], [0012], [0035] and Fig. 1, the apparatus is identified as gNB).
Claim Rejections – 35 USC§ 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 2-4, 7-11, 18, 24 and 25 are rejected under 35 U.S.C. 103 as being unpatentable over Kela ‘875 in view of Ryden et al(US 2024/0292236 A1).
Regarding claim 2, Kela ‘875 teaches all of the claim limitations above except, wherein the one or more processors are further configured to cause the apparatus to: generate the report based on a report configuration associated with the QoS type, wherein the report indicates a set of predicted channel metrics for a first set of beams or a set of confidence levels associated with the set of predicted channel metrics.
Ryden ‘236 teaches, wherein the one or more processors are further configured to cause the apparatus to: generate the report based on a report configuration associated with the QoS type([0096], , [0168], [0187] and Fig. 9, the E determines whether to predict radio signal measurements and subsequently transmit a report, such as reporting a new strongest beam index, based on local criteria that includes QoS targets, thus the generation and transmission of the prediction report is configured by and dependent upon the specific QoS target of the UE), wherein the report indicates a set of predicted channel metrics for a first set of beams or a set of confidence levels associated with the set of predicted channel metrics([0085], [0086], [0096] and Figs. 3b, 4, using a denoising autoencoder(DAE) to predict radio signal measurements, such as RSRTP metrics, for unmeasured SSB beams. The UE then used these predicted beam metrics to generate and transmit a report, such as indicating the new strongest SSB beam index to the network).
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify the communication system of Ryden ‘236, by incorporating the teaching of Ryden ‘236, since such modification would provide a method/apparatus that improves UE energy efficiency by predicting a radio signal measurement, as suggested by Ryden ‘236([0007]).
Regarding claim 3, the combination of Kela ‘875 and Ryden ‘236 teaches all of the claim limitations above, Kela ‘875 further teaches, wherein the one or more processors are further configured to cause the apparatus to: select a communication configuration associated with the QoS type([0047], [0048] and Fig. 5, selecting a transmission/reception (Tx/Rx) beam to use as the communication configuration for an upcoming transmission time interval(TTI) this configuration selection is based on the highest scheduling priority metric, which corresponds directly to the highest priority QoS class of the scheduled data); and
output, for transmission to the network entity, data based on the communication configuration, or obtain, from the network entity, other data based on the communication configuration([0037], [0047] and Fig. 1, using the selected ML beam communication configuration for upcoming uplink (UL) or downlink(DL) data transmissions between the UE and the network node. For example, data is communicated over the physical channel (PUSCHO using the specific beam configuration established for the QoS type).
Regarding claim 4, the combination of Kela ‘875 and Ryden ‘236 teaches all of the claim limitations above, Kela ‘875 further teaches, wherein communication configuration comprises: a subset of beams of the first set of beams([0037], [0040], [0047] and Figs. 1-2, 5, establishing a communication configuration by selecting a specific serving beam or a subset of beams from a larger pool of candidate beams. This selected sunset of beams is determined by the machine learning model to best serve the UE for upcoming communications), wherein the data is output for transmission via the subset of beams and the other data is obtained via the subset of beams([0037], [0047] and figs. 1, 5, using the selected ML beam to perform both uplink (UL) data transmissions and downlink(DL) data receptions in the upcoming transmission time interval(TTI)).
Regarding claim 7, the combination of Kela ‘875 and Ryden ‘236 teaches all of the claim limitations above, Kela ‘875 further teaches, wherein the subset of beams includes a beam with a highest predicted channel metric in the set of predicted channel metrics([0040] and Fig. 2, ML model using Q-learning that calculates predicted reward(Q-values) for candidate beams and uses an “argmax” mathematical function to select the next beam action. The argmax function ensures that the selected beam for communication is the one associated with the highest predicted performance).
Regarding claim 8, the combination of Kela ‘875 and Ryden ‘236 teaches all of the claim limitations above, Kela ‘875 further teaches, wherein the one or more processors are further configured to cause the apparatus to: obtain, from the network entity, an indication of a subset of beams selected from the first set of beams based on the report( [0037], [0047] and Fig. 1, the network node(gNB) selects a serving beam based on the channel state information (CSI) reporting provided by the UE. The network node then transmits an indication back to the UE, as the UE receiving an allocation for selected beam for use in upcoming operations);
and output, for transmission to the network entity, data via the subset of beams, or obtain, from the network entity, other data via the subset of beams([0037], [0047] and Fig. 1, once the beam allocation is received, the selected beam is used for upcoming uplink(UL) and downlink (DL) data transmissions between the UE and the network node. Data is actively transmitted (output) or received (obtained) via physical channels such as PUSCH or PUCCH over the selected beam).
Regarding claim 9, the combination of Kela ‘875 and Ryden ‘236 teaches all of the claim limitations above, Ryden ‘236 further teaches, wherein the one or more beam prediction procedures comprise: obtaining a set of reference signals from the network entity; determining one or more channel metrics for a second set of beams based on the set of reference signals; and predicting, based on the one or more channel metrics for the second set of beams and the output of the machine learning model, the set of predicted channel metrics or the set of confidence levels associated with the set of predicted channel metrics([005], [0035], [0080] and Figs. 3, receiving a set of SSB reference beams and measuring a subset of those beams to determine their initial channel metrics. The UE then uses a ML model, specifically a DAE , to predict the remaining unmeasured radio signal measurements based on the initial measured metrics).
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify the communication system of Ryden ‘236, by incorporating the teaching of Ryden ‘236, since such modification would provide a method/apparatus that improves UE energy efficiency by predicting a radio signal measurement, as suggested by Ryden ‘236([0007]).
Regarding claim 10, the combination of Kela ‘875 and Ryden ‘236 teaches all of the claim limitations above, Ryden ‘236 further teaches, wherein a confidence level of the set of confidence levels indicates an accuracy of a predicted channel metric in the set of predicted channel metrics or a probability that a corresponding predicted channel metric of a beam in the first set of beams satisfies a threshold channel metric value at a future time([0087], [0088], [0110] and Fig. 3, signaling candidate noising patterns along with their associated prediction performance, detailing the mean prediction accuracy and variance to indicate the accuracy of the predicted beam metrics. The system also evaluates these prediction to determine whether the mean average reconstruction error satisfies a specific accuracy threshold).
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify the communication system of Ryden ‘236, by incorporating the teaching of Ryden ‘236, since such modification would provide a method/apparatus that improves UE energy efficiency by predicting a radio signal measurement, as suggested by Ryden ‘236([0007]).
Regarding claim 11, the combination of Kela ‘875 and Ryden ‘236 teaches all of the claim limitations above, Ryden ‘236 further teaches, wherein a predicted channel metric in the set of predicted channel metrics is associated with at least one of a reference signal received power (RSRP), a reference signal received quality (RSRQ), a sounding reference signal (SRS), a received signal strength indicator (RSSI), or a signal-to-noise and interference (SINR) ratio([0080], the measured and predicted radio signal measurement utilized by the ML models comprise RSRP data for the SSB beams of a cell).
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify the communication system of Ryden ‘236, by incorporating the teaching of Ryden ‘236, since such modification would provide a method/apparatus that improves UE energy efficiency by predicting a radio signal measurement, as suggested by Ryden ‘236([0007]).
Regarding claim 18, Kela ‘875 teaches all of the claim limitations above except, wherein the report indicates a set of predicted channel metrics for a first set of beams or a set of confidence levels associated with the set of predicted channel metrics.
Ryden ‘236 teaches, wherein the report indicates a set of predicted channel metrics for a first set of beams or a set of confidence levels associated with the set of predicted channel metrics([0085], [0086], [0096] and Figs. 3b, 4, using a denoising autoencoder(DAE) to predict radio signal measurements, such as RSRTP metrics, for unmeasured SSB beams. The UE then used these predicted beam metrics to generate and transmit a report, such as indicating the new strongest SSB beam index to the network).
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify the communication system of Ryden ‘236, by incorporating the teaching of Ryden ‘236, since such modification would provide a method/apparatus that improves UE energy efficiency by predicting a radio signal measurement, as suggested by Ryden ‘236([0007]).
Regarding claim 24, the combination of Kela ‘875 and Ryden ‘236 teaches all of the claim limitations above, Kela ‘875 further teaches, wherein the one or more processors are further configured to cause the apparatus to: select a subset of beams from the first set of beams([0037], [0047] an Figs. 1, 5, the network node (gNB) selecting a specific serving beam, which is a subset of the available candidate beams); select a communication configuration for the subset of beams based on the report and a QoS type from the set of QoS types([0037], [0047] an Figs. 1, 5, the gNB establishing the communication configuration by selecting the specific Tx/Rx beam based on both the UE’s CSI reporting and the highest scheduling priority metric);
and output, for transmission to the UE, an indication of the subset of beams and the communication configuration([0037], [0047] an Figs. 1, after the beam selection process the network node transmits an indication of the established configuration back to the UE as shown with the signal flow diagram a network node transmitting an allocation for selected beam to the UE for upcoming communications).
Regarding claim 25, the combination of Kela ‘875 and Ryden ‘236 teaches all of the claim limitations above, Kela ‘875 further teaches, wherein the one or more processors are further configured to cause the apparatus to: output, for transmission to the UE, a set of reference signals to be used for determining channel metrics for a second set of beams([0035], [0038] and Fig. 1, the network (gNB) transmitting reference signals to the UE, “SS Burst/CSI-RS”. The UE then measures these SSB and CSI reference signals to determine beam measurement quantities and channel metrics).
Claims 5 and 6 are rejected under 35 U.S.C. 103 as being unpatentable over Kela ‘875 and Ryden ‘236 as applied to claims above, and further in view of Veijalainen et al(US 2024/0048219 A1) hereinafter referred as Ve ‘219.
Regarding claim 5, the combination of Kela ‘875 and Ryden ‘236 teaches all of the claim limitations above except, wherein the subset of beams includes at least two beams and the data is output, for transmission to the network entity, via a diversity scheme involving the at least two beams.
Ve ‘219 teaches, wherein the subset of beams includes at least two beams and the data is output, for transmission to the network entity, via a diversity scheme involving the at least two beams([0129], [0182] and Fig. 6, configuring the UE with at least two beams, an ML selected firs beam and a second “anchor beam” using two unified TCI states, which functions as a diversity scheme, where data is transmitted on the first beam and the anchor beam acts as a spatial fallback path for data re- transmissions if the first transmission fails).
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify the communication system of Ryden ‘236, by incorporating the teaching of Ve ‘219, since such modification would enable a UE to perform g data packet retransmission on a second beam when transmission on a first beam has failed, as suggested by Ve ‘219([0002]).
Regarding claim 6, the combination of Kela ‘875, Ryden ‘236 and Ve ‘219 teaches all of the claim limitations, Ve ‘219 further teaches, wherein a first beam of the at least two beams is associated with a highest predicted channel metric in the set of predicted channel metrics and a second beam of the at least two beams is associated with a second highest channel metric in the set of predicted channel metrics([0124], [0148] and Fig. 4, selecting a first beam having the highest Q-value(highest predicted channel metric) output by the ML model and selecting eh second anchor beam by using an N-bit indicator for the Kth strongest beam from previously reported measurements, which supports configuring the second highest metric when K =2).
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify the communication system of Ryden ‘236, by incorporating the teaching of Ve ‘219, since such modification would enable a UE to perform g data packet retransmission on a second beam when transmission on a first beam has failed, as suggested by Ve ‘219([0002]).
Claim 13 is rejected under 35 U.S.C. 103 as being unpatentable over Kela ‘875 in view of Zhu et al(US 2025/0007597 A1).
Regarding claim 13, Kela ‘875 teaches all of the claim limitations above except, wherein the one or more processors are further configured to cause the apparatus to: obtain, from the network entity, a report triggering signal, wherein the report is output for transmission based on the report triggering signal.
Zhu ‘597 teaches, wherein the one or more processors are further configured to cause the apparatus to: obtain, from the network entity, a report triggering signal ([0061], [0074] and Fig. 4, the network device communicates a DCI trigger to the UE),
wherein the report is output for transmission based on the report triggering signal ([0074] and Fig. 4, the transmission of the CSI-RS resource selecting signals (the report) from the UE is triggered by the network node’s DCI. In direct response to the trigger signal, the UE outputs an transmits the election signal through physical uplink channels like PUCCH or PUSCH).
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify the communication system of Ryden ‘236, by incorporating the teaching of Zhu ‘597, since such modification would enable a UE to indicate channel state information-reference signal (CSI-RS) resource information from a CSI-RS resource table based on the predicted beam, and communicating, by the UE to the network device, selected CSI-RS resource information, as suggested by Zhu ‘597([0005]).
Claims 14 and 26-28 are rejected under 35 U.S.C. 103 as being unpatentable over Kela ‘875 in view of Wang et al(US 2022/0353803 A1).
Regarding claim 14, Kela ‘875 teaches all of the claim limitations except, wherein the one or more processors are further configured to cause the apparatus to:: obtain, from the network entity, an indication related to QoS of the data scheduled to be communicated with the network entity, wherein the QoS type is identified based on a defined rule or the indication related to QoS of the data scheduled to be communicated with the network entity
Wang ‘803 teaches, wherein the one or more processors are further configured to cause the apparatus to: obtain, from the network entity, an indication related to QoS of the data scheduled to be communicated with the network entity([0087], [0095], [0097] and Fig.7-8, the network sends the UE an available -architecture message identifying architectures associates with a network slice that satisfies the application’s requested QoS level. Downlink packets marked with QoS fowl identifiers also provide additional network originating QoS indication), wherein the QoS type is identified based on a defined rule or the indication related to QoS of the data scheduled to be communicated with the network entity([0079], [0097], [0098] and Fig.6, 8, the network sends ML-architecture selection rules to the UE and the UE applies those rules to derive the requested QoS level from the application’s performance requirements).
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify the communication system of Ryden ‘236, by incorporating the teaching of Wang ‘803, since such modification enable the UE and the network-slice manager to work together, to determine an appropriate machine-learning architecture that satisfies a quality-of-service level associated with the application and form a portion of an end-to-end machine-learning architecture that meets the quality-of-service level requested by the application, as suggested by Wang ‘803([0002]).
Regarding claim 26, Kela ‘875 teaches all of the claim limitations except, wherein the one or more processors are further configured to cause the apparatus to: output, for transmission to the UE, an indication of a QoS type from the set of QoS types.
Wang ‘803 teaches, wherein the one or more processors are further configured to cause the apparatus to: output, for transmission to the UE, an indication of a QoS type from the set of QoS types([0087], [0095], [0097] and Fig.7-8, the network sends the UE an available -architecture message identifying architectures associates with a network slice that satisfies the application’s requested QoS level. Downlink packets marked with QoS fowl identifiers also provide additional network originating QoS indication).
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify the communication system of Ryden ‘236, by incorporating the teaching of Wang ‘803, since such modification enable the UE and the network-slice manager to work together, to determine an appropriate machine-learning architecture that satisfies a quality-of-service level associated with the application and form a portion of an end-to-end machine-learning architecture that meets the quality-of-service level requested by the application, as suggested by Wang ‘803([0002]).
Regarding claim 27, the combination of Kela ‘875 and Wang ‘803 teaches all of the claim limitations, Kela ‘875 further teaches, wherein the QoS type indicates a data priority or a traffic type of data scheduled to be communicated with the UE([0031], [0048], defining the QoS types by data priority (e.g. highest priority QoS data flows) and traffic flow or traffic type, e.g. differentiating between eMBB full buffer traffic, URLLC and best effort QoS classes).
Ke875
Regarding claim 28, the combination of Kela ‘875 and Wang ‘803 teaches all of the claim limitations, Kela ‘875 further teaches,, wherein the report is obtained at a defined periodicity based on a report configuration associated with the QoS type([0041], [0042], [0048], outputting the CSI report at a defined periodicity(e.g. establishing a constantly 5 ms periodicity). It further teaches utilizing different CSI reporting parameters-including different reporting periodicity based on the models phase, where eh models and parameters are configured for each QoS class or QoS class group).
Claim 21 is rejected under 35 U.S.C. 103 as being unpatentable over Kela ‘875 in view of Ve ‘219.
Regarding claim 21, Kela ‘875 teaches all of the claim limitations except, wherein the subset of beams includes at least two beams and the data is obtained, from the UE, based on a diversity scheme involving the at least two beams.
Ve ‘219 teaches, wherein the subset of beams includes at least two beams and the data is obtained, from the UE, based on a diversity scheme involving the at least two beams ([0129], [0182] and Fig. 6, configuring the UE with at least two beanms, an ML selected firs beam and a second “anchor beam” using two unified TCI states, which functions as a diversity scheme, where data is transmitted on the first beam and the anchor beam acts as a spatial fallback path for data re- transmissions if the first transmission fails).
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify the communication system of Ryden ‘236, by incorporating the teaching of Ve ‘219, since such modification would enable a UE to perform g data packet retransmission on a second beam when transmission on a first beam has failed, as suggested by Ve ‘219([0002]).
Claim 29 is rejected under 35 U.S.C. 103 as being unpatentable over Kela ‘875 in view of Yin(US 2023/0125945 A1).
Regarding claim 29, Kela ‘875 teaches all of the claim limitations except, wherein the QoS type indicates a physical layer (PHY) priority of the report.
Yin ‘945 teaches, wherein the QoS type indicates a physical layer (PHY) priority of the report([0032], [0046], [0049] and Figs. 1, 2, different service types (such as URLLC versus eMBB) are associated with distinct priority indices(index 3 for low priority, index 1 for high priority) at the physical layer to govern PUCCH/PUSCH report handling. A URLLC service type report is assigned a higher physical layer priority to differentiate it from standard eMBB reports).
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify the communication system of Ryden ‘236, by incorporating the teaching of Yin ‘945, since such modification would provide enhancement of a CSI report for URLLC such that a CSI report for URLLC can be differentiated from a CSI report for enhanced Mobile Broad-Band (eMBB) by assigning a higher priority to the CSI report for URLLC or a CSI report for URLLC can be provided with specific periodicity and reliability requirements, as suggested by Yin ‘945([0001]).
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/AWET HAILE/Primary Examiner, Art Unit 2474
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